Title :
A fast exact parallel implementation of the k-nearest neighbour pattern classifier
Author_Institution :
Dept. of Electron. Syst. Eng., Essex Univ., Colchester, UK
Abstract :
A neural network architecture is presented that precisely implements the k-nearest-neighbour (k-NN) pattern classification rule. Given n exemplars, the size of the architecture grows O(n) and the time taken per classification grows O(log n). This offers perhaps the most useful neural implementation of the k-NN classifier compared to previous implementations, which suffer either from worst-case exponential training time, excessively large networks, unpredictable classification times, or inexact implementations of the classification rule
Keywords :
computational complexity; neural net architecture; parallel architectures; pattern classification; fast exact parallel implementation; k-nearest neighbour pattern classifier; neural network architecture; Artificial neural networks; Computer architecture; Computer networks; Hardware; Neural networks; Parallel architectures; Parallel machines; Pattern classification; Pattern recognition; Systems engineering and theory;
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4859-1
DOI :
10.1109/IJCNN.1998.687142